SoroushVahidi/scidocs huggingface dataset:
Comparison of SQL, NoSQL and NewSQL databases for internet of things
(2016)

The Internet of Things (IoT) in all its essentiality is a collection of sensors bridged together tightly. The present day development in the technology industry has thus elevated the emphasis of large amounts of data. IoT is expected to generate and collect an enormous amount of data from varied locations very quickly. The concerns for features like storage and performance factors are coercing the databases to outperform themselves. Traditional relational databases have time and again proved to be efficient. NoSQL and more recently NewSQL databases having gained an impetus are asserted to perform better than the classic SQL counterpart. This paper compares the performance of three databases each from the said technologies; SQL (MySQL), NoSQL (MongoDB) and NewSQL (VoltDB) respectively for sensor readings. The sensor data handled ranges in vast (MB to GB) size and tested against: single write, single read, single delete and multi write operations.
Agenda-Based User Simulation for Bootstrapping a POMDP Dialogue System
(2007)

This paper investigates the problem of bootstrapping a statistical dialogue manager without access to training data and proposes a new probabilistic agenda-based method for simulating user behaviour. In experiments with a statistical POMDP dialogue system, the simulator was realistic enough to successfully test the prototype system and train a dialogue policy. An extensive study with human subjects showed that the learned policy was highly competitive, with task completion rates above 90%. 1 Background and Introduction 1.1 Bootstrapping Statistical Dialogue Managers One of the key advantages of a statistical approach to Dialogue Manager (DM) design is the ability to formalise design criteria as objective reward functions and to learn an optimal dialogue policy from real dialogue data. In cases where a system is designed from scratch, however, it is often the case that no suitable in-domain data is available for training the DM. Collecting dialogue data without a working prototype is problematic, leaving the developer with a classic chicken-and-egg problem. Wizard-of-Oz (WoZ) experiments can be carried out to record dialogues, but they are often time-consuming and the recorded data may show characteristics of humanhuman conversation rather than typical human-computer dialogue. Alternatively, human-computer dialogues can be recorded with a handcrafted DM prototype but neither of these two methods enables the system designer to test the implementation of the statistical DM and the learning algorithm. Moreover, the size of the recorded corpus (typically 10 dialogues) usually falls short of the requirements for training a statistical DM (typically 10 dialogues). 1.2 User Simulation-Based Training In recent years, a number of research groups have investigated the use of a two-stage simulation-based setup. A statistical user model is first trained on a limited amount of dialogue data and the model is then used to simulate dialogues with the interactively learning DM (see Schatzmann et al. (2006) for a literature review). The simulation-based approach assumes the presence of a small corpus of suitably annotated in-domain dialogues or out-of-domain dialogues with a matching dialogue format (Lemon et al., 2006). In cases when no such data is available, handcrafted values can be assigned to the model parameters given that the model is sufficiently simple (Levin et al., 2000; Pietquin and Dutoit, 2005) but the performance of dialogue policies learned this way has not been evaluated using real users.
A tomographic formulation of spotlight-mode synthetic aperture radar
(1983)

Spotlight-mode synthetic aperture radar (spotlight-mode SAR) synthesizes high-resolution terrain maps using data gathered from multiple observation angles. This paper shows that spotlight-mode SAR can be interpreted as a tomographic reeonstrution problem and analyzed using the projection-slice theorem from computer-aided tomograpy (CAT). The signal recorded at each SAR transmission point is modeled as a portion of the Fourier transform of a central projection of the imaged ground area. Reconstruction of a SAR image may then be accomplished using algorithms from CAT. This model permits a simple understanding of SAR imaging, not based on Doppler shifts. Resolution, sampling rates, waveform curvature, the Doppler effect, and other issues are also discussed within the context of this interpretation of SAR.
An Accelerator for High Efficient Vision Processing
(2017)

In recent years, neural network accelerators have been shown to achieve both high energy efficiency and high performance for a broad application scope within the important category of recognition and mining applications. Still, both the energy efficiency and performance of such accelerators remain limited by memory accesses. In this paper, we focus on image applications, arguably the most important category among recognition and mining applications. The neural networks which are state-of-the-art for these applications are convolutional neural networks (CNNs), and they have an important property: weights are shared among many neurons, considerably reducing the neural network memory footprint. This property allows to entirely map a CNN within an SRAM, eliminating all DRAM accesses for weights. By further hoisting this accelerator next to the image sensor, it is possible to eliminate all remaining DRAM accesses, i.e., for inputs and outputs. In this paper, we propose such a CNN accelerator, placed next to a CMOS or CCD sensor. The absence of DRAM accesses combined with a careful exploitation of the specific data access patterns within CNNs allows us to design an accelerator which is highly energy-efficient. We present a single-core implementation down to the layout at 65 nm, with a modest footprint of 5.94mm $^{\boldsymbol {2}}$  and consuming only 336mW, but still about  $\boldsymbol {30\times }$  faster than high-end GPUs. For visual processing with higher resolution and frame-rate requirements, we further present a multicore implementation with elevated performance.
Adaptive Control of KNTU Planar Cable-Driven Parallel Robot with Uncertainties in Dynamic and Kinematic Parameters
(2015)

This paper addresses the design and implementation of adaptive control on a planar cable-driven parallel robot with uncertainties in dynamic and kinematic parameters. To develop the idea, firstly, adaptation is performed on dynamic parameters and it is shown that the controller is stable despite the kinematic uncertainties. Then, internal force term is linearly separated into a regressor matrix in addition to a kinematic parameter vector that contains estimation error. In the next step to improve the controller performance, adaptation is performed on both the dynamic and kinematic parameters. It is shown that the performance of the proposed controller is improved by correction in the internal forces. The proposed controller not only keeps all cables in tension for the whole workspace of the robot, it is computationally simple and it does not require measurement of the end-effector acceleration as well. Finally, the effectiveness of the proposed control algorithm is examined through some experiments on KNTU planar cable-driven parallel robot and it is shown that the proposed control algorithm is able to provide suitable performance in practice.
Integration of Text and Audio Features for Genre Classification in Music Information Retrieval
(2007)

Multimedia content can be described in versatile ways as its essence is not limited to one view. For music data these multiple views could be a song’s audio features as well as its lyrics. Both of these modalities have their advantages as text may be easier to search in and could cover more of the ‘content semantics’ of a song, while omitting other types of semantic categorisation. (Psycho)acoustic feature sets, on the other hand, provide the means to identify tracks that ‘sound similar’ while less supporting other kinds of semantic categorisation. Those discerning characteristics of different feature sets meet users’ differing information needs. We will explain the nature of text and audio feature sets which describe the same audio tracks. Moreover, we will propose the use of textual data on top of low level audio features for music genre classification. Further, we will show the impact of different combinations of audio features and textual features based on content words.
Extraction of high-resolution frames from video sequences
(1996)

The human visual system appears to be capable of temporally integrating information in a video sequence in such a way that the perceived spatial resolution of a sequence appears much higher than the spatial resolution of an individual frame. While the mechanisms in the human visual system that do this are unknown, the effect is not too surprising given that temporally adjacent frames in a video sequence contain slightly different, but unique, information. This paper addresses the use of both the spatial and temporal information present in a short image sequence to create a single high-resolution video frame. A novel observation model based on motion compensated subsampling is proposed for a video sequence. Since the reconstruction problem is ill-posed, Bayesian restoration with a discontinuity-preserving prior image model is used to extract a high-resolution video still given a short low-resolution sequence. Estimates computed from a low-resolution image sequence containing a subpixel camera pan show dramatic visual and quantitative improvements over bilinear, cubic B-spline, and Bayesian single frame interpolations. Visual and quantitative improvements are also shown for an image sequence containing objects moving with independent trajectories. Finally, the video frame extraction algorithm is used for the motion-compensated scan conversion of interlaced video data, with a visual comparison to the resolution enhancement obtained from progressively scanned frames.
The Big Bang: Facial Trauma Caused by Recreational Fireworks.
(2015)

In the Netherlands, it is a tradition of setting off fireworks to celebrate the turn of the year. In our medical facility, each year patients with severe skeletal maxillofacial trauma inflicted by recreational fireworks are encountered. We present two cases of patients with severe blast injury to the face, caused by direct impact of rockets, and thereby try to contribute to the limited literature on facial blast injuries, their treatment, and clinical outcome. These patients require multidisciplinary treatment, involving multiple reconstructive surgeries, and the overall recovery process is long. The severity of these traumas raises questions about the firework traditions and legislations not only in the Netherlands but also worldwide. Therefore, the authors support restrictive laws on personal use of fireworks in the Netherlands.
A State-Space Approach to Dynamic Nonnegative Matrix Factorization
(2015)

Nonnegative matrix factorization (NMF) has been actively investigated and used in a wide range of problems in the past decade. A significant amount of attention has been given to develop NMF algorithms that are suitable to model time series with strong temporal dependencies. In this paper, we propose a novel state-space approach to perform dynamic NMF (D-NMF). In the proposed probabilistic framework, the NMF coefficients act as the state variables and their dynamics are modeled using a multi-lag nonnegative vector autoregressive (N-VAR) model within the process equation. We use expectation maximization and propose a maximum-likelihood estimation framework to estimate the basis matrix and the N-VAR model parameters. Interestingly, the N-VAR model parameters are obtained by simply applying NMF. Moreover, we derive a maximum a posteriori estimate of the state variables (i.e., the NMF coefficients) that is based on a prediction step and an update step, similarly to the Kalman filter. We illustrate the benefits of the proposed approach using different numerical simulations where D-NMF significantly outperforms its static counterpart. Experimental results for three different applications show that the proposed approach outperforms two state-of-the-art NMF approaches that exploit temporal dependencies, namely a nonnegative hidden Markov model and a frame stacking approach, while it requires less memory and computational power.
Standalone OPC UA Wrapper for Industrial Monitoring and Control Systems
(2018)

OPC unified architecture (UA), a communication standard for the manufacturing industry, enables exchanging control and management data among distributed entities in industrial automation systems. OPC UA wrapper is a migration strategy that provides UA clients with seamless access to legacy servers having OPC classic interfaces. This paper presents the design of a standalone OPC UA wrapper and discusses its performance through extensive experiments using a prototype implementation. The wrapper consists of two main components, i.e., UA server and classic client, which communicate with each other via shared memory and semaphore. One important feature of the design is that it employs a distributed component object model runtime library implemented in Java for platform independence. This makes it possible to build a cost-competitive wrapper system by using commercial off-the-shelf non-Windows solutions with low-cost microprocessors. Another key feature is the event-driven update interface between the UA and classic components, which we propose as an alternative to the sampling-based mechanism for the reduced delay. Through experiments using workloads from an industrial monitoring system, we present a systematic approach of identifying the system parameters having a direct impact on the wrapper performance and eventually tuning them such that the read and subscription services of OPC UA exhibit the best performance.
Real-time Action Recognition with Dissimilarity-based Training of Specialized Module Networks
(2018)

This paper addresses the problem of real-time action recognition in trimmed videos, for which deep neural networks have defined the state-of-the-art performance in the recent literature. For attaining higher recognition accuracies with efficient computations, researchers have addressed the various aspects of limitations in the recognition pipeline. This includes network architecture, the number of input streams (where additional streams augment the color information), the cost function to be optimized, in addition to others. The literature has always aimed, though, at assigning the adopted network (or networks, in case of multiple streams) the task of recognizing the whole number of action classes in the dataset at hand. We propose to train multiple specialized module networks instead. Each module is trained to recognize a subset of the action classes. Towards this goal, we present a dissimilarity-based optimized procedure for distributing the action classes over the modules, which can be trained simultaneously offline. On two standard datasets–UCF-101 and HMDB-51–the proposed method demonstrates a comparable performance, that is superior in some aspects, to the state-of-the-art, and that satisfies the real-time constraint. We achieved 72.5% accuracy on the challenging HMDB-51 dataset. By assigning fewer and unalike classes to each module network, this research paves the way to benefit from light-weight architectures without compromising recognition accuracy1.
Edge Computing and IoT Based Research for Building Safe Smart Cities Resistant to Disasters
(2017)

Recently, several researches concerning with smart and connected communities have been studied. Soon the 4G / 5G technology becomes popular, and cellular base stations will be located densely in the urban space. They may offer intelligent services for autonomous driving, urban environment improvement, disaster mitigation, elderly/disabled people support and so on. Such infrastructure might function as edge servers for disaster support base. In this paper, we enumerate several research issues to be developed in the ICDCS community in the next decade in order for building safe, smart cities resistant to disasters. In particular, we focus on (A) up-to-date urban crowd mobility prediction and (B) resilient disaster information gathering mechanisms based on the edge computing paradigm. We investigate recent related works and projects, and introduce our on-going research work and insight for disaster mitigation.
Predicting clicks: estimating the click-through rate for new ads
(2007)

Search engine advertising has become a significant element of the Web browsing experience. Choosing the right ads for the query and the order in which they are displayed greatly affects the probability that a user will see and click on each ad. This ranking has a strong impact on the revenue the search engine receives from the ads. Further, showing the user an ad that they prefer to click on improves user satisfaction. For these reasons, it is important to be able to accurately estimate the click-through rate of ads in the system. For ads that have been displayed repeatedly, this is empirically measurable, but for new ads, other means must be used. We show that we can use features of ads, terms, and advertisers to learn a model that accurately predicts the click-though rate for new ads. We also show that using our model improves the convergence and performance of an advertising system. As a result, our model increases both revenue and user satisfaction.
GeoDa: An Introduction to Spatial Data Analysis
(2006)

This article presents an overview of GeoDa, a free software program intended to serve as a user-friendly and graphical introduction to spatial analysis for nongeographic information systems (GIS) specialists. It includes functionality ranging from simple mapping to exploratory data analysis, the visualization of global and local spatial autocorrelation, and spatial regression. A key feature of GeoDa is an interactive environment that combines maps with statistical graphics, using the technology of dynamically linked windows. A brief review of the software design is given, as well as some illustrative examples that highlight distinctive features of the program in applications dealing with public health, economic development, real estate analysis, and criminology.
Exploratory analysis of spatial and temporal data - a systematic approach
(2005)

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How Maps Work - Representation, Visualization, and Design
(2004)

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Local Indicators of Spatial Association
(2003)

The capabilities for visualization, rapid data retrieval, and manipulation in geographic information systems (GIS) have created the need for new techniques of exploratory data analysis that focus on the "spatial" aspects of the data. The identification of local patterns of spatial association is an important concern in this respect. In this paper, I outline a new general class of local indicators of spatial association (LISA) and show how they allow for the decomposition of global indicators, such as Moran's I, into the contribution of each observation. The LISA statistics serve two purposes. On one hand, they may be interpreted as indicators of local pockets of nonstationarity, or hot spots, similar to the Gi and G; statistics of Getis and Ord (1992). On the other hand, they may be used to assess the influence of individual locations on the magnitude of the global statistic and to identify "outliers," as in Anselin's Moran scatterplot (1993a). An initial evaluation of the properties of a LISA statistic is carried out for the local Moran, which is applied in a study of the spatial pattern of conflict for African countries and in a number of Monte Carlo simulations.
Detection of Influential Observation in Linear Regression
(2000)

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Clause Restructuring for Statistical Machine Translation
(2005)

We describe a method for incorporating syntactic information in statistical machine translation systems. The first step of the method is to parse the source language string that is being translated. The second step is to apply a series of transformations to the parse tree, effectively reordering the surface string on the source language side of the translation system. The goal of this step is to recover an underlying word order that is closer to the target language word-order than the original string. The reordering approach is applied as a pre-processing step in both the training and decoding phases of a phrase-based statistical MT system. We describe experiments on translation from German to English, showing an improvement from 25.2% Bleu score for a baseline system to 26.8% Bleu score for the system with reordering, a statistically significant improvement.
Temporal Convolutional Neural Networks for Diagnosis from Lab Tests
(2015)

Early diagnosis of treatable diseases is essential for improving healthcare, and many diseases’ onsets are predictable from annual lab tests and their temporal trends. We introduce a multi-resolution convolutional neural network for early detection of multiple diseases from irregularly measured sparse lab values. Our novel architecture takes as input both an imputed version of the data and a binary observation matrix. For imputing the temporal sparse observations, we develop a flexible, fast to train method for differentiable multivariate kernel regression. Our experiments on data from 298K individuals over 8 years, 18 common lab measurements, and 171 diseases show that the temporal signatures learned via convolution are significantly more predictive than baselines commonly used for early disease diagnosis.